CycleGAN

[project page] [paper]

Torch implementation for learning an image-to-image translation (i.e. pix2pix) without input-output pairs, for example:

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Jun-Yan Zhu*, Taesung Park*, Phillip Isola, Alexei A. Efros
Berkeley AI Research Lab, UC Berkeley
In arxiv, 2017. (* equal contributions)

This package includes CycleGAN, pix2pix, as well as other methods like BiGAN/ALI and Apple's paper S+U learning.
PyTorch version is coming soon (by April 7th).

Applications

Monet Paintings to Photos

Collection Style Transfer

Object Transfiguration

Season Transfer

Photo Enhancement: iPhone photo to DSLR photo

Setup

Prerequisites

Linux or OSX
NVIDIA GPU + CUDA CuDNN (CPU mode and CUDA without CuDNN may work with minimal modification, but untested)

Getting Started

Install torch and dependencies from https://github.com/torch/distro
Install torch packages nngraph, class, display

luarocks install nngraph
luarocks install class
luarocks install https://raw.githubusercontent.com/szym/display/master/display-scm-0.rockspec

Clone this repo:

git clone https://github.com/junyanz/CycleGAN
cd CycleGAN

Download the dataset (e.g. zebra and horse images from ImageNet):

bash ./datasets/download_dataset.sh horse2zebra

Train the model

DATA_ROOT=./datasets/horse2zebra name=horse2zebra_model th train.lua

(CPU only) The same training command without using a GPU or CUDNN. Setting the environment variables gpu=0 cudnn=0 forces CPU only

DATA_ROOT=./datasets/horse2zebra name=horse2zebra_model gpu=0 cudnn=0 th train.lua

(Optionally) start the display server to view results as the model trains. (See Display UI for more details):

th -ldisplay.start 8000 0.0.0.0

Finally, test the model:

DATA_ROOT=./datasets/horse2zebra name=horse2zebra_model phase=test th test.lua

The test results will be saved to a html file here: ./results/horse2zebra_model/latest_test/index.html.

Train

DATA_ROOT=/path/to/data/ name=expt_name th train.lua

Models are saved to ./checkpoints/expt_name (can be changed by passing checkpoint_dir=your_dir in train.lua).

See opt_train in options.lua for additional training options.

Test

DATA_ROOT=/path/to/data/ name=expt_name which_direction='AtoB' phase=test th test.lua

This will run the model named expt_name in both directions on all images in /path/to/data/testA and /path/to/data/testB. If which_direction is 'BtoA', the two sets A and B of the datasets are flipped.

Result images, and a webpage to view them, are saved to ./results/expt_name (can be changed by passing results_dir=your_dir in test.lua).

See opt_test in options.lua for additional testing options.

Datasets

Download the datasets using the following script:

bash ./datasets/download_dataset.sh dataset_name

cityscapes: 2975 images from the Cityscapes training set.
maps: 1096 training images scraped from Google Maps.
horse2zebra: 939 horse images and 1177 zebra images downloaded from ImageNet using keywords wild horse and zebra
apple2orange: 996 apple images and 1020 orange images downloaded from ImageNet using keywords apple and navel orange.
summer2winter_yosemite: 1273 summer Yosemite images and 854 winter Yosemite images were downloaded using Flickr API. See more details in our paper.
monet2photo, vangogh2photo, ukiyoe2photo, cezanne2photo: The art images were downloaded from Wikiart. The real photos are downloaded from Flickr using combination of tags landscape and landscapephotography. The training set size of each class is Monet:1074, Cezanne:584, Van Gogh:401, Ukiyo-e:1433, Photographs:6853.
iphone2dslr_flower: both classe of images were downlaoded from Flickr. The training set size of each class is iPhone:1813, DSLR:3316. See more details in our paper.

Pre-trained Models

Download the pre-trained models with the following script. You need to rename the model (e.g. orange2apple to /checkpoints/orange2apple/latest_net_G.t7) after the download has finished.

bash ./models/download_model.sh model_name

orange2apple (orange -> apple) and apple2orange: trained on the CMP Facades dataset.
horse2zebra (horse -> zebra) and zebra2horse (zebra -> horse): trained on the CMP Facades dataset.
style_monet (landscape photo -> Monet painting style), style_vangogh (landscape photo -> Van Gogh painting style), style_ukiyoe (landscape photo -> Ukiyo-e painting style), style_cezanne (landscape photo -> Cezanne painting style): trained on paintings and Flickr landscape photos.
monet2photo (Monet paintings -> real landscape): trained on paintings and Flickr landscape photos.
cityscapes_photo2label (street scene -> label) and cityscapes_label2photo (label -> street scene): trained on the Cityscapes dataset.
map2sat (map -> aerial photo) and sat2map (aerial photo -> map): trained on Google maps.
iphone2dslr_flower (iPhone photos of flower -> DSLR photos of flower): trained on Flickr photos.

For example, to generate Ukiyo-e style images using the pre-trained model,

bash ./datasets/download_dataset.ukiyoe2photo
bash ./models/download_model.sh style_ukiyoe
mkdir ./checkpoints/ukiyoe2photo_pretrained
mv ./models/style_ukiyoe.t7 ./checkpoints/ukiyoe2photo_pretrained/latest_net_G.t7
DATA_ROOT=./datasets/ukiyoe2photo name=ukiyoe2photo_pretrained which_direction='BtoA' model=one_direction_test phase=test th test.lua

Please pay attention to the direction. which_direction='BtoA' was used because the pretrained network transforms photos to Ukiyo-e-style images, but the dataset ukiyoe2photo is from Ukiyo-e paintings to photos. model=one_direction_test loads the code that generates outputs of the trained network in only one direction.

Display UI

Optionally, for displaying images during training and test, use the display package.

Install it with: luarocks install https://raw.githubusercontent.com/szym/display/master/display-scm-0.rockspec
Then start the server with: th -ldisplay.start
Open this URL in your browser: http://localhost:8000

By default, the server listens on localhost. Pass 0.0.0.0 to allow external connections on any interface:

th -ldisplay.start 8000 0.0.0.0

Then open http://(hostname):(port)/ in your browser to load the remote desktop.

Citation

If you use this code for your research, please cite our paper:

@article{CycleGAN2017,
  title={Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networkss},
  author={Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A},
  journal={arXiv preprint arXiv:1703.10593},
  year={2017}
}

Related Projects:

pix2pix: Image-to-image translation using conditional adversarial nets
iGAN: Interactive Image Generation via Generative Adversarial Networks

Cat Paper Collection

If you love cats, and love reading cool graphics, vision, and learning papers, please check out the Cat Paper Collection:
[Github] [Webpage]

Acknowledgments

Code borrows from pix2pix and DCGAN. The data loader is modified from DCGAN and Context-Encoder. The generative network is adopted from neural-style with Instance Normalization.

frialex / cyclegan Goto Github PK

cyclegan's Introduction